石油与天然气化工2025，Vol.54Issue(3)：146-152,7.DOI:10.3969/j.issn.1007-3426.2025.03.020
基于规则的天然气净化典型设备知识抽取方法
Rule-based knowledge extraction method for typical equipment in natural gas purification
摘要
Abstract
Objective The production process of natural gas purification is characterized by flammability,explosiveness,continuity,and complexity,all of which pose substantial safety risks.Fault attribution and tracing are crucial for operators to identify potential hazards,prevent accidents,and guarantee safe production.Furthermore,they provide invaluable guidance for engineering personnel in their operations.Knowledge graphs can efficiently store and manage the vast amount of process technical regulations and fault handling logs in chemical production,offering data support for downstream tasks such as fault tracing and enhancing the efficiency of maintenance personnel.However,most existing production and maintenance data are recorded and stored in unstructured text data,curtailing the potential for directly constructing a knowledge graph.To address this challenge,this study proposes a knowledge extraction method that combines a bidirectional long short-term memory network(BiLSTM)with conditional random field(CRF)and rule matching.Method Firstly,production or operation and maintenance data pertaining to relevant industrial processes are gathered as original data and subjected to preprocessing.Secondly,the method integrating BiLSTM-CRF and rule matching was employed for knowledge extraction.Finally,the extracted data was stored in the graph database.Result In this study,the flash tank device in a natural gas purification plant was taken as an example.The constructed knowledge graph was basically consistent with the theoretical graph structure constructed by expert experience.Conclusion The proposed model can effectively extract knowledge about the device's production operation and maintenance data.The constructed knowledge graph enhances the data readability and facilitates ease of query and learning for operation and maintenance personnel.关键词
天然气净化/长短时记忆网络/条件随机场/命名实体识别/知识抽取/知识图谱Key words
natural gas purification/bidirectional long short-term memory network/conditional random field/named entity recognition/knowledge extraction/knowledge graph引用本文复制引用
纪天浩,彭传波,裴爱霞,周健,刘持强,李大字..基于规则的天然气净化典型设备知识抽取方法[J].石油与天然气化工,2025,54(3):146-152,7.基金项目
国家自然科学基金项目"基于图网络的复杂过程系统深度强化学习方法研究"(62273026) （62273026）
